CN112967819A - Preoperative evaluation method and system for neurosurgery - Google Patents
Preoperative evaluation method and system for neurosurgery Download PDFInfo
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- CN112967819A CN112967819A CN202110250668.1A CN202110250668A CN112967819A CN 112967819 A CN112967819 A CN 112967819A CN 202110250668 A CN202110250668 A CN 202110250668A CN 112967819 A CN112967819 A CN 112967819A
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Abstract
The invention discloses a preoperative evaluation method and a preoperative evaluation system for neurosurgery, wherein a first operation scheme is obtained through first user information; obtaining first surgical assessment information according to a first surgical plan; inputting the first operation scheme and the first user historical information into an index matching model to obtain first matching index information; obtaining a first user evaluation rule according to the first operation evaluation information and the first matching index information; obtaining first and second evaluation rule information according to the first user evaluation rule; obtaining a first user detection requirement according to the first evaluation rule information; generating a first user evaluation questionnaire according to the second evaluation rule information; obtaining a first evaluation result according to a first user detection requirement; obtaining a second evaluation result according to the first user evaluation questionnaire; and obtaining a first user preoperative evaluation report according to the first evaluation result and the second evaluation result. The technical problems that assessment results are not comprehensive enough and reliability is unstable due to the fact that assessment before an operation depends on experience and inspection data of doctors are solved.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a preoperative evaluation method and a preoperative evaluation system for neurosurgery.
Background
Neurosurgery (neurosurgey) is a branch of surgery, and is a research method of Neurosurgery, which is based on surgery as a main treatment means, and is used for researching the injury, inflammation, tumor, deformity and certain genetic metabolic disorders or dysfunction diseases of human nervous systems, such as brain, spinal cord and peripheral nervous system, and related auxiliary mechanisms, such as skull, scalp, cerebral meninges and other structures, by applying a unique neurosurgical research method, such as: the etiology and pathogenesis of epilepsy, Parkinson's disease, neuralgia and other diseases, and explores a high, precise and advanced discipline of new diagnosis, treatment and prevention technology. The preoperative evaluation provides an opportunity for patients and family members to know the operation process, risk and effect, and the preoperative evaluation also provides effective guarantee for the normal operation of the operation because the neurosurgery has certain risk difficulty.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
in the prior art, preoperative evaluation of neurosurgery depends on experience and inspection data of doctors, is greatly influenced by the level of the doctors, lacks objective and comprehensive evaluation standards, and has the technical problems of incomplete evaluation result and unstable reliability.
Disclosure of Invention
The embodiment of the application provides a preoperative assessment method and a preoperative assessment system for neurosurgery, and solves the technical problems that in the prior art, preoperative assessment of neurosurgery depends on experience and inspection data of doctors, is greatly influenced by the level of the doctors, lacks objective and comprehensive assessment standards, and has incomplete assessment results and unstable reliability.
In view of the above problems, embodiments of the present application provide a preoperative evaluation method and system for neurosurgery.
In a first aspect, the present embodiments provide a preoperative assessment method for neurosurgery, the method comprising: obtaining first user information; obtaining a first operation scheme of a first user according to the first user information; acquiring first user history information according to the first user information; obtaining first surgical assessment information based on the first user's first surgical plan; inputting the first operation scheme of the first user and the historical information of the first user into an index matching model to obtain first matching index information; obtaining a first user evaluation rule according to the first operation evaluation information and the first matching index information; obtaining first evaluation rule information and second evaluation rule information according to the first user evaluation rule, wherein the first evaluation rule information has a first attribute; obtaining a first user detection requirement according to the first evaluation rule information; generating a first user evaluation questionnaire according to the second evaluation rule information; obtaining a first evaluation result according to the first user detection requirement and a first user evaluation rule; obtaining a second evaluation result according to the first user evaluation questionnaire and the first user evaluation rule; and obtaining the pre-operation assessment report of the first user according to the first assessment result and the second assessment result.
In another aspect, the present application also provides a preoperative evaluation system for neurosurgery, the system comprising:
a first obtaining unit configured to obtain first user information;
a second obtaining unit, configured to obtain a first surgical plan of a first user according to the first user information;
a third obtaining unit, configured to obtain first user history information according to the first user information;
a fourth obtaining unit configured to obtain first surgical assessment information according to the first surgical plan of the first user;
a first execution unit, configured to input the first surgical plan of the first user and the first user history information into an index matching model, and obtain first matching index information;
a fifth obtaining unit, configured to obtain a first user evaluation rule according to the first surgical evaluation information and the first matching index information;
a sixth obtaining unit, configured to obtain first evaluation rule information and second evaluation rule information according to the first user evaluation rule, where the first evaluation rule information has a first attribute and the second evaluation rule information has a second attribute;
a seventh obtaining unit, configured to obtain a first user detection requirement according to the first evaluation rule information;
an eighth obtaining unit configured to generate a first user evaluation questionnaire according to the second evaluation rule information;
a ninth obtaining unit, configured to obtain a first evaluation result according to the first user detection requirement and a first user evaluation rule;
a tenth obtaining unit, configured to obtain a second evaluation result according to the first user evaluation questionnaire and the first user evaluation rule;
an eleventh obtaining unit, configured to obtain the pre-operation assessment report of the first user according to the first assessment result and the second assessment result.
In a third aspect, the present invention provides a preoperative evaluation system for neurosurgery comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of the first aspect are implemented when the program is executed by the processor.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the embodiment of the application provides a preoperative evaluation method and a preoperative evaluation system for neurosurgery, which are used for evaluating the neurosurgery by obtaining first user information; obtaining a first operation scheme of a first user according to the first user information; acquiring first user history information according to the first user information; obtaining first surgical assessment information based on the first user's first surgical plan; inputting the first operation scheme of the first user and the historical information of the first user into an index matching model to obtain first matching index information; obtaining a first user evaluation rule according to the first operation evaluation information and the first matching index information; obtaining first evaluation rule information and second evaluation rule information according to the first user evaluation rule, wherein the first evaluation rule information has a first attribute; obtaining a first user detection requirement according to the first evaluation rule information; generating a first user evaluation questionnaire according to the second evaluation rule information; obtaining a first evaluation result according to the first user detection requirement and a first user evaluation rule; obtaining a second evaluation result according to the first user evaluation questionnaire and the first user evaluation rule; and obtaining the pre-operation assessment report of the first user according to the first assessment result and the second assessment result. Generating corresponding detection requirements according to index data in the first evaluation rule information, obtaining corresponding detection results according to the detection requirements, extracting corresponding index results according to the response content of the first user evaluation questionnaire, comparing the results with the index requirements determined in the first user evaluation rule, converting the results into qualified rates according to the difference values and the quantitative standards of the evaluation rules, summarizing the evaluation indexes of all the evaluation indexes, performing overall physical evaluation, and finally generating a preoperative evaluation report of the first user, wherein the preoperative evaluation report comprises the comparison relation between all the detected index contents and schemes, and the contents of the overall preoperative evaluation results and the like are included, so that index analysis is performed by using the convenience and comprehensiveness of big data in combination with an operation scheme, the influence parameter information of operation influence is determined from the index analysis, and the evaluation rules are specifically formulated by combining the physical characteristics of the user and historical medical records, the comprehensiveness of data and the personal difference are effectively combined, the comprehensiveness of an assessment range is guaranteed, the accuracy of personal fitting degree is guaranteed to be higher, the dependence on the personal level of a doctor is avoided, the stability of an analysis result is improved by means of scientific system analysis processing of the data, meanwhile, a mathematical model is added, the operation speed and the accuracy of the result are improved, the preoperative assessment efficiency of neurosurgery is effectively improved, and the technical effect of comprehensive and accurate index setting is achieved. Therefore, the technical problems that the preoperative evaluation of the neurosurgery depends on the experience and the inspection data of a doctor, the influence of the level of the doctor is large, the objective and comprehensive evaluation standard is lacked, the evaluation result is not comprehensive enough, and the reliability is unstable in the prior art are solved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for pre-operative evaluation of neurosurgery in accordance with an embodiment of the present application;
FIG. 2 is a schematic structural diagram of a pre-operative evaluation system for neurosurgery according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a first executing unit 15, a fifth obtaining unit 16, a sixth obtaining unit 17, a seventh obtaining unit 18, an eighth obtaining unit 19, a ninth obtaining unit 20, a tenth obtaining unit 21, an eleventh obtaining unit 22, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 305.
Detailed Description
The embodiment of the application provides a preoperative assessment method and a preoperative assessment system for neurosurgery, and solves the technical problems that in the prior art, preoperative assessment of neurosurgery depends on experience and inspection data of doctors, is greatly influenced by the level of the doctors, lacks objective and comprehensive assessment standards, and has incomplete assessment results and unstable reliability. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
Neurosurgery (neurosurgey) is a branch of surgery, and is a research method of Neurosurgery, which is based on surgery as a main treatment means, and is used for researching the injury, inflammation, tumor, deformity and certain genetic metabolic disorders or dysfunction diseases of human nervous systems, such as brain, spinal cord and peripheral nervous system, and related auxiliary mechanisms, such as skull, scalp, cerebral meninges and other structures, by applying a unique neurosurgical research method, such as: the etiology and pathogenesis of epilepsy, Parkinson's disease, neuralgia and other diseases, and explores a high, precise and advanced discipline of new diagnosis, treatment and prevention technology. The preoperative evaluation provides an opportunity for patients and family members to know the operation process, risk and effect, and the preoperative evaluation also provides effective guarantee for the normal operation of the operation because the neurosurgery has certain risk difficulty. However, in the prior art, the neurosurgical preoperative evaluation depends on experience and inspection data of doctors, is greatly influenced by the level of the doctors, lacks objective and comprehensive evaluation standards, and has the technical problems of incomplete evaluation result and unstable reliability.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
obtaining first user information; obtaining a first operation scheme of a first user according to the first user information; acquiring first user history information according to the first user information; obtaining first surgical assessment information based on the first user's first surgical plan; inputting the first operation scheme of the first user and the historical information of the first user into an index matching model to obtain first matching index information; obtaining a first user evaluation rule according to the first operation evaluation information and the first matching index information; obtaining first evaluation rule information and second evaluation rule information according to the first user evaluation rule, wherein the first evaluation rule information has a first attribute; obtaining a first user detection requirement according to the first evaluation rule information; generating a first user evaluation questionnaire according to the second evaluation rule information; obtaining a first evaluation result according to the first user detection requirement and a first user evaluation rule; obtaining a second evaluation result according to the first user evaluation questionnaire and the first user evaluation rule; and obtaining the pre-operation assessment report of the first user according to the first assessment result and the second assessment result. The method has the advantages that convenience and comprehensiveness of big data are utilized to carry out index analysis in combination with an operation scheme, influence parameter information of operation influence is determined, meanwhile, an evaluation rule is formulated in combination with physical characteristics of a user and pertinence of historical medical records, comprehensiveness of data and personal differences are effectively combined, comprehensiveness of an evaluation range is guaranteed, meanwhile, higher accuracy of personal fitting degree is guaranteed, dependence on personal level of doctors is avoided, stability of analysis results is improved by means of scientific system analysis and processing of data, operation speed and accuracy of the results are improved by adding a mathematical model, preoperative evaluation efficiency of neurosurgery is effectively improved, and the technical effect that index setting is comprehensive and accurate is achieved.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, the present application provides a preoperative assessment method of neurosurgery, the method comprising:
step S100: obtaining first user information;
specifically, the user who performs preoperative evaluation by using the method needs to enter personal data in advance to apply for login, and the first user information is the personal identity information, data acquisition permission and the like of the first user. The identity screening of the first user and the searching and grabbing of the personal information in the big data can be performed by utilizing the first user information, so that the convenience and the reliability of a data source are improved, and the personal privacy of the user is ensured.
Step S200: obtaining a first operation scheme of a first user according to the first user information;
specifically, a first surgical scheme of the first user is screened out according to the personal identity information of the first user, the first surgical scheme is surgical scheme data which is synchronized with data of the first user, the surgical scheme data comprises specific surgical scheme information of an evaluation surgery, and the specific surgical scheme information comprises specific contents of surgical steps, surgical staff, medical staff information, used surgical instruments, surgical medicines and the like.
Step S300: acquiring first user history information according to the first user information;
specifically, the first user history information is history medical record information of the first user, and includes a physical quality report, examination data, a family disease condition, a history treatment record of the first user, and various medical data which can be extracted from big data. The historical treatment record and the current basic physical indexes of the first user can be mastered through the historical information of the first user, and the physical quality of the first user is evaluated.
Step S400: obtaining first surgical assessment information based on the first user's first surgical plan;
further, in the step S400 of obtaining first surgical assessment information according to the first surgical plan of the first user, in this embodiment of the present application, the step S includes:
step S410: obtaining first surgical procedure information according to a first surgical plan of the first user;
step S420: obtaining a step difficulty coefficient according to the first operation step information;
step S430: judging whether the step difficulty coefficient meets a first preset condition or not;
step S440: when the first operation step information is satisfied, acquiring step history record information according to the first operation step information;
step S450: acquiring step influence parameters according to the step history record information;
step S460: obtaining a parameter influence coefficient according to the step influence parameter;
step S470: and obtaining the first operation evaluation information according to the step influence parameters and the parameter influence coefficients.
Specifically, the surgical risk in the first surgical plan is determined according to the specific content of the first surgical plan of the first user and the disease condition data of the first user, the instruments and medicines needed by the steps are determined according to the surgical steps in the first surgical plan, the surgical difficulty, risk coefficients and the like of the steps are known, the surgical risk and the like can be analyzed and processed through historical experiments and clinical data, the operation can be performed through a method for constructing a mathematical model for improving the operation speed and accuracy, the historical, clinical and experimental data of the relevant steps are mastered through big data retrieval or retrieval of the system and local data of keywords, the analysis of the influencing parameters is performed through the historical data, the parameters needing to be evaluated in advance in the steps are found, the steps with low difficulty coefficients in the surgical steps can be skipped, and the first preset condition is set to be small enough, the method mainly screens the steps without risks, avoids unnecessary calculation and analysis, such as operation preparation work and the like, finds the influence parameters which have influence on the operation and are generated in the steps according to the historical record information of the operation steps, analyzes the theoretical influence parameters of the steps according to academic data, clinical data and the like besides analyzing the influence parameters according to the historical record because whether the sources of the historical record information completely influence the accuracy of the analysis of the influence parameters, determines the influence coefficients of the parameters on the operation steps according to all determined influence parameters and the action and the relation in the operation steps, and finally forms first operation evaluation information according to the influence parameter information and the parameter influence coefficients. In addition, in order to improve the accuracy of the data, when the historical records are analyzed, the personal data of the doctor can be used for data retrieval to obtain similar operation records, so that the method has a reference meaning, and the influence parameters are analyzed and determined according to the historical records of the doctor, so that the accuracy of the parameters is improved, and the method is more suitable for the practical use environment.
Step S500: inputting the first operation scheme of the first user and the historical information of the first user into an index matching model to obtain first matching index information;
further, the step S500 of inputting the first surgical plan of the first user and the historical information of the first user into an index matching model to obtain first matching index information includes:
step S510: taking the first user first surgical plan as first input information;
step S520: taking the first user history information as second input information;
step S530: inputting the first input information and the second input information into an index matching model, wherein the index matching model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: the first input information, the second input information, and identification information identifying the first matching index information;
step S540: obtaining output information of the index matching model, wherein the output information comprises the first matching index information.
Specifically, for users with different physical qualities, the same surgical plan has differences in the emphasis and content of evaluation, so that the physical state of the first user is determined according to the first user historical information, that is, the physical examination data, medical record information, and historical treatment information of the first user, and parameter matching is performed on the current surgical plan, and index content required to be evaluated for the personal physical state of the first user is found from the parameter matching, the first matching index information is obtained by matching the influence index existing in the first surgical plan with the physical state of the first user, the first user has a problem with the index, which is just the index required to be noticed and correlated in the first surgical plan, in order to improve the accuracy of the matching parameter result, the embodiment of the application constructs a neural network model for processing, and performs operation processing by using a mathematical model, the index matching model is a Neural network model in machine learning, and the Neural Network (NN) is a complex Neural network system formed by widely interconnecting a large number of simple processing units (called neurons), reflects many basic characteristics of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (Artificial Neural Networks) are a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. And inputting the first input information and the second input information into a neural network model through training of a large amount of training data, and outputting first matching index information.
More specifically, the training process is essentially a supervised learning process, each group of supervised data includes the first input information, the second input information and identification information for identifying the first matching index information, the first input information and the second input information are input into a neural network model, the neural network model performs continuous self-correction and adjustment according to the identification information for identifying the first matching index information, and the present group of supervised learning is ended until the obtained output result is consistent with the identification information, and the next group of supervised learning is performed; and when the output information of the neural network model reaches the preset accuracy rate/reaches the convergence state, finishing the supervised learning process. Through the supervised learning of the neural network model, the neural network model can process the input information more accurately, so that more accurate and suitable first matching index information can be obtained, the preoperative evaluation of the first user can be effectively performed, index analysis can be performed on the physical condition of the first user through the analysis of historical records and the combination of an operation scheme, the individual state of the user can be fitted, meanwhile, the efficiency and the accuracy of a data operation processing result are improved by adding the neural network model, and a basis is tamped for providing more accurate physical index evaluation.
Step S600: obtaining a first user evaluation rule according to the first operation evaluation information and the first matching index information;
further, in the step S600 of the embodiment of the present application, the obtaining a first user evaluation rule according to the first surgical evaluation information and the first matching index information includes:
step S610: obtaining a matching index name and matching index data according to the first matching index information;
step S620: obtaining a surgical index requirement according to the first surgical assessment information and the matching index name;
step S630: acquiring an index deviation value according to the matching index data and the operation index requirement;
step S640: acquiring an index influence degree according to the index deviation value and the first operation evaluation information;
step S650: judging whether the index influence degree meets a second preset condition or not;
step S660: and when the first user evaluation rule is satisfied, obtaining the first user evaluation rule according to the index deviation value, the matched index name and the index influence degree.
Specifically, comprehensive analysis is performed according to the index name and index data included in the first matching index information and the index content required to be evaluated in the first operation evaluation information, so as to determine which indexes are required by the first user to perform targeted analysis processing, and as the neurosurgical operation is different according to the operation condition and is directed to different preoperative examinations, the physical condition of the user needs to be matched one by one, so that the most comprehensive index analysis is obtained, the energy of the patient cannot be influenced too much, and the operation condition cannot be influenced too little, for example, the nervous system evaluation may change the requirement of anesthesia, and the consciousness disorder may aggravate the existing lung collapse and requires postoperative auxiliary mechanical ventilation. A respiratory system method, wherein a user has obstructive or restrictive lung diseases, the occurrence probability of respiratory system complications in an operation period is increased, and before the operation, lung function examination and arterial blood gas analysis are performed to improve the respiratory function; urinary system, patients with acidosis due to renal failure before operation, and patients with acidosis must be tested if assisted ventilation is needed after operation. Therefore, the first user evaluation rule determined according to the first operation evaluation information and the first matching index information is an evaluation rule customized for the physical characteristics of the first user.
Step S700: obtaining first evaluation rule information and second evaluation rule information according to the first user evaluation rule, wherein the first evaluation rule information has a first attribute;
further, the obtaining, according to the first user evaluation rule, first evaluation rule information and second evaluation rule information, where the first evaluation rule information has a first attribute, in step S700 in this embodiment of the present application, includes:
step S710: obtaining a first user index name list according to the first user evaluation rule, wherein the first user index name list comprises N first user index names, and N is a natural number greater than 1;
step S720: according to all the first user index names, obtaining historical index source information respectively;
step S730: obtaining the first evaluation rule information according to the first attribute and the historical source information of the index;
step S740: and obtaining the second evaluation rule information according to the first user evaluation rule and the first evaluation rule information.
Step S800: obtaining a first user detection requirement according to the first evaluation rule information;
step S900: generating a first user evaluation questionnaire according to the second evaluation rule information;
specifically, the requirements in the evaluation rule are classified according to the index acquisition source corresponding to the first user evaluation rule, and the data can be directly acquired through a system and cannot be directly acquired through the system, for example, the inspection data can be directly acquired by synchronizing the data through communication connection, or the big data can be screened and retrieved by keyword screening, some indexes are contents required to be provided by the user, such as recent medicine taking history, since the taking of some medicines can affect the progress of the operation, such as that a patient takes a blood anticoagulation medicine before an operation for a period of time or a medicine with a long half-life period can affect the operation, the indexes cannot be directly acquired through the data, the indexes are divided into two categories according to different acquisition sources, the first attribute can be acquired through data searching and screening, and the other indexes are required to be provided by the user, the first evaluation rule information is obtained from the system data screening interface according to the index name or the code, the second evaluation rule information customizes the first user evaluation questionnaire according to the content of the evaluation index and the personal characteristics of the first user, and the extraction of the corresponding index result is carried out according to the reply content of the first user evaluation questionnaire.
Step S1000: obtaining a first evaluation result according to the first user detection requirement and a first user evaluation rule;
step S1100: obtaining a second evaluation result according to the first user evaluation questionnaire and the first user evaluation rule;
step S1200: and obtaining the pre-operation assessment report of the first user according to the first assessment result and the second assessment result.
Specifically, the corresponding detection requirements are generated according to the index data in the first evaluation rule information, the corresponding detection results are obtained according to the detection requirements, the corresponding index results are extracted according to the response content of the first user evaluation questionnaire, the results are compared with the index requirements determined in the first user evaluation rule, the results are converted into the qualified rate according to the difference and the quantitative standard of the evaluation rule, the evaluation indexes of all the evaluation indexes are collected, the overall physical evaluation is carried out, the preoperative evaluation report of the first user is finally generated, the preoperative evaluation report of all the detected index contents and the preoperative comparison relationship of the scheme are included, the contents of the overall preoperative evaluation results and the like are included, the index analysis is carried out by using the convenience and the comprehensiveness of big data in combination with the operation scheme, the influence parameter information of the operation influence is determined from the index analysis, and the evaluation rule is specifically formulated by combining the physical characteristics of the user and the historical, the comprehensiveness of data and the personal difference are effectively combined, the comprehensiveness of an assessment range is guaranteed, the accuracy of personal fitting degree is guaranteed to be higher, the dependence on the personal level of a doctor is avoided, the stability of an analysis result is improved by means of scientific system analysis processing of the data, meanwhile, a mathematical model is added, the operation speed and the accuracy of the result are improved, the preoperative assessment efficiency of neurosurgery is effectively improved, and the technical effect of comprehensive and accurate index setting is achieved. Therefore, the technical problems that the preoperative evaluation of the neurosurgery depends on the experience and the inspection data of a doctor, the influence of the level of the doctor is large, the objective and comprehensive evaluation standard is lacked, the evaluation result is not comprehensive enough, and the reliability is unstable in the prior art are solved.
Further, the method further comprises:
step 1310: obtaining first user historical operation information according to the first user historical information;
step S1320: obtaining operation relevance according to the historical operation information of the first user and the first operation scheme of the first user;
step S1330: obtaining a correlation screening standard according to the operation correlation;
step S1340: obtaining a first user historical operation screening result according to the relevance screening standard and the operation relevance of the first user historical operation information;
step S1350: obtaining operation influence data according to the first user historical operation screening result;
step S1360: obtaining second surgery evaluation information according to the surgery influence data and the first surgery evaluation information;
step S1370: and obtaining the first user evaluation rule according to the second operation evaluation information and the first matching index information.
Specifically, the first user historical operation information, which is an operation record previously made by the first user, is known according to the first user historical operation information, if the first user historical operation information includes a plurality of operations, the first user historical operation information can be analyzed according to the direct relevance between the first operation scheme of the first user and the historical operations, the operation with higher relevance is analyzed, the specific relevance is determined according to the historical operation condition of the first user, if the historical operations of the first user are more and the relevance difference between the first operation scheme and the first operation scheme is larger, the operation with higher relevance is selected, the operation with lower relevance is screened, if the historical operation records of the first user are less, the set screening condition is low, the current historical operation record is utilized as much as possible for analyzing the problems appearing in the historical operation record, the index influence corresponding to the problems is shown, the index influence is obtained by the first user, the historical operation information is obtained by the first user, and the analysis is performed according to the historical operation, The body state influence is correspondingly analyzed, if the blood sugar of a user in the historical operation record influences the operation result, the control level of the blood sugar is positioned, the operation influence data of the current time is positioned, the first user evaluation rule is obtained according to the analysis of the historical operation record and the current first operation evaluation information, omission in evaluation indexes is avoided, and due to the fact that some users are normal in the inspection indexes and sudden situations can occur in the operation, the problems occurring in the historical operation are utilized to conduct targeted analysis, the reliability and the comprehensiveness of evaluation contents are improved, and the actual body condition of the user is fitted. The technical problems that in the prior art, preoperative evaluation of neurosurgery depends on experience and inspection data of doctors, the evaluation is greatly influenced by the level of the doctors, objective and comprehensive evaluation standards are lacked, evaluation results are not comprehensive enough, and reliability is not stable are further solved.
Further, after obtaining the first user historical surgery information according to the first user historical information, the method includes:
step S1410: when the historical operation information of the first user does not exist, obtaining first associated user information according to the first user information;
step S1420: obtaining first associated historical surgical information according to the first associated user information;
step S1430: when the first associated historical operation information exists, obtaining first associated historical information according to the first associated user information;
step S1440: obtaining a first user association degree according to the first user history information and the first association history information;
step S1450: judging whether the first user association degree meets a third preset condition or not;
step S1460: when the first relevant historical operation screening result is met, relevant operation influence data are obtained according to the first relevant historical operation screening result;
step S1470: and obtaining the second operation evaluation information according to the related operation influence data and the first operation evaluation information.
Specifically, for the condition that the first user has no historical operation record, finding out the associated user according to the relationship of the first user, wherein the first associated user information is the user who has a blood relationship and a genetic relationship with the first user, such as parents, brothers and sisters, grandparents and the like, searching whether the historical operation record exists according to the first associated user information, if so, further determining the association degree between the associated user and the first user, if the association degree meets the requirement, further analyzing the association degree between the illness state of the operation record and the first historical information of the first user, namely the physical index and the illness state of the first user, and using the association degree of the first user to express to judge whether the association degree of the first user meets the set requirement, wherein the higher the association degree of the first user indicates that the referential property is higher, and the lower the association of the two operations is the lower the reference value, and adding the index data meeting the requirement of the association degree into the second operation evaluation information of the first user as a reference so as to improve the reliability and comprehensiveness of the evaluation content and better fit the actual physical condition of the user. The technical problems that in the prior art, preoperative evaluation of neurosurgery depends on experience and inspection data of doctors, the evaluation is greatly influenced by the level of the doctors, objective and comprehensive evaluation standards are lacked, evaluation results are not comprehensive enough, and reliability is not stable are further solved.
Example two
Based on the same inventive concept as the preoperative evaluation method of neurosurgery in the foregoing embodiment, the present invention also provides a preoperative evaluation system of neurosurgery, as shown in fig. 2, the system comprising:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain first user information;
a second obtaining unit 12, wherein the second obtaining unit 12 is configured to obtain a first surgical plan of a first user according to the first user information;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain first user history information according to the first user information;
a fourth obtaining unit 14, the fourth obtaining unit 14 being configured to obtain first surgical assessment information according to the first surgical plan of the first user;
a first executing unit 15, where the first executing unit 15 is configured to input the first surgical plan of the first user and the first user history information into an index matching model, and obtain first matching index information;
a fifth obtaining unit 16, where the fifth obtaining unit 16 is configured to obtain a first user evaluation rule according to the first surgical evaluation information and the first matching index information;
a sixth obtaining unit 17, configured to obtain first evaluation rule information and second evaluation rule information according to the first user evaluation rule, where the first evaluation rule information has a first attribute and the second evaluation rule information has a second attribute;
a seventh obtaining unit 18, where the seventh obtaining unit 18 is configured to obtain a first user detection requirement according to the first evaluation rule information;
an eighth obtaining unit 19, where the eighth obtaining unit 19 is configured to generate a first user evaluation questionnaire according to the second evaluation rule information;
a ninth obtaining unit 20, where the ninth obtaining unit 20 is configured to obtain a first evaluation result according to the first user detection requirement and a first user evaluation rule;
a tenth obtaining unit 21, where the tenth obtaining unit 21 is configured to obtain a second evaluation result according to the first user evaluation questionnaire and the first user evaluation rule;
an eleventh obtaining unit 22, where the eleventh obtaining unit 22 is configured to obtain the first pre-user assessment report according to the first assessment result and the second assessment result.
Further, the system further comprises:
an eleventh obtaining unit configured to obtain first surgical procedure information according to the first surgical plan of the first user;
a twelfth obtaining unit configured to obtain a procedure difficulty coefficient from the first surgical procedure information;
a first judging unit, configured to judge whether the step difficulty coefficient satisfies a first predetermined condition;
a thirteenth obtaining unit configured to obtain step history information based on the first surgical step information when satisfied;
a fourteenth obtaining unit configured to obtain a step influence parameter according to the step history information;
a fifteenth obtaining unit, configured to obtain a parameter influence coefficient according to the step influence parameter;
a sixteenth obtaining unit, configured to obtain the first surgical assessment information according to the step influence parameter and the parameter influence coefficient.
Further, the system further comprises:
a seventeenth obtaining unit, configured to obtain a matching index name and matching index data according to the first matching index information;
an eighteenth obtaining unit, configured to obtain a surgical index requirement according to the first surgical assessment information and the matching index name;
a nineteenth obtaining unit, configured to obtain an index deviation value according to the matching index data and the surgical index requirement;
a twentieth obtaining unit, configured to obtain an index influence degree according to the index deviation value and the first surgical evaluation information;
a second judgment unit configured to judge whether the index influence degree satisfies a second predetermined condition;
a twenty-first obtaining unit, configured to, when satisfied, obtain the first user evaluation rule according to the index deviation value, the matching index name, and the index influence degree.
Further, the system further comprises:
a twenty-second obtaining unit, configured to obtain a first user index name list according to the first user evaluation rule, where the first user index name list includes N first user index names, and N is a natural number greater than 1;
a twenty-third obtaining unit, configured to obtain historical index source information according to all the first user index names respectively;
a twenty-fourth obtaining unit, configured to obtain the first evaluation rule information according to the first attribute and the index history source information;
a twenty-fifth obtaining unit, configured to obtain the second evaluation rule information according to the first user evaluation rule and the first evaluation rule information.
Further, the system further comprises:
a twenty-sixth obtaining unit, configured to obtain first user historical surgery information according to the first user historical information;
a twenty-seventh obtaining unit, configured to obtain a surgical relevance according to the historical surgical information of the first user and the first surgical plan of the first user;
a twenty-eighth obtaining unit, configured to obtain an association screening criterion according to the surgical association;
a twenty-ninth obtaining unit, configured to obtain a first user historical surgery screening result according to the relevance screening criterion and the surgery relevance of the first user historical surgery information;
a thirtieth obtaining unit, configured to obtain surgical influence data according to the first user historical surgical screening result;
a thirty-first obtaining unit, configured to obtain second surgical assessment information according to the surgical influence data and the first surgical assessment information;
a thirty-second obtaining unit, configured to obtain the first user evaluation rule according to the second surgical evaluation information and the first matching index information.
Further, the system further comprises:
a thirty-third obtaining unit, configured to obtain first associated user information according to the first user information when the first user historical surgery information does not exist;
a thirty-fourth obtaining unit, configured to obtain first associated historical surgical information according to the first associated user information;
a thirty-fifth obtaining unit, configured to obtain first association history information according to the first association user information when the first association history operation information exists;
a thirty-sixth obtaining unit, configured to obtain a first user association degree according to the first user history information and the first association history information;
the first judging unit is used for judging whether the first user association degree meets a third preset condition or not;
a thirty-seventh obtaining unit, configured to, when satisfied, obtain relevant surgery influence data according to the first relevant historical surgery screening result;
a thirty-eighth obtaining unit, configured to obtain the second surgical assessment information according to the relevant surgical impact data and the first surgical assessment information.
Further, the system further comprises:
a second execution unit for taking the first user first surgical plan as first input information;
a third execution unit configured to take the first user history information as second input information;
a first input unit, configured to input the first input information and the second input information into an index matching model, where the index matching model is obtained by training multiple sets of training data, and each of the multiple sets of training data includes: the first input information, the second input information, and identification information identifying the first matching index information;
a thirty-ninth obtaining unit configured to obtain output information of the index matching model, the output information including the first matching index information.
Various modifications and embodiments of the aforementioned preoperative evaluation method for neurosurgery in the first embodiment of fig. 1 are also applicable to the preoperative evaluation system for neurosurgery in the present embodiment, and the implementation method of the preoperative evaluation system for neurosurgery in the present embodiment is clear to those skilled in the art from the foregoing detailed description of the preoperative evaluation method for neurosurgery, so for the brevity of the description, detailed description is omitted here.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of a preoperative evaluation method for neurosurgery in the aforementioned embodiments, the present invention also provides a preoperative evaluation system for neurosurgery, on which a computer program is stored, which program, when executed by a processor, implements the steps of any one of the methods of a preoperative evaluation method for neurosurgery described above.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the embodiment of the application provides a preoperative evaluation method and a preoperative evaluation system for neurosurgery, which are used for evaluating the neurosurgery by obtaining first user information; obtaining a first operation scheme of a first user according to the first user information; acquiring first user history information according to the first user information; obtaining first surgical assessment information based on the first user's first surgical plan; inputting the first operation scheme of the first user and the historical information of the first user into an index matching model to obtain first matching index information; obtaining a first user evaluation rule according to the first operation evaluation information and the first matching index information; obtaining first evaluation rule information and second evaluation rule information according to the first user evaluation rule, wherein the first evaluation rule information has a first attribute; obtaining a first user detection requirement according to the first evaluation rule information; generating a first user evaluation questionnaire according to the second evaluation rule information; obtaining a first evaluation result according to the first user detection requirement and a first user evaluation rule; obtaining a second evaluation result according to the first user evaluation questionnaire and the first user evaluation rule; and obtaining the pre-operation assessment report of the first user according to the first assessment result and the second assessment result. Generating corresponding detection requirements according to index data in the first evaluation rule information, obtaining corresponding detection results according to the detection requirements, extracting corresponding index results according to the response content of the first user evaluation questionnaire, comparing the results with the index requirements determined in the first user evaluation rule, converting the results into qualified rates according to the difference values and the quantitative standards of the evaluation rules, summarizing the evaluation indexes of all the evaluation indexes, performing overall physical evaluation, and finally generating a preoperative evaluation report of the first user, wherein the preoperative evaluation report comprises the comparison relation between all the detected index contents and schemes, and the contents of the overall preoperative evaluation results and the like are included, so that index analysis is performed by using the convenience and comprehensiveness of big data in combination with an operation scheme, the influence parameter information of operation influence is determined from the index analysis, and the evaluation rules are specifically formulated by combining the physical characteristics of the user and historical medical records, the comprehensiveness of data and the personal difference are effectively combined, the comprehensiveness of an assessment range is guaranteed, the accuracy of personal fitting degree is guaranteed to be higher, the dependence on the personal level of a doctor is avoided, the stability of an analysis result is improved by means of scientific system analysis processing of the data, meanwhile, a mathematical model is added, the operation speed and the accuracy of the result are improved, the preoperative assessment efficiency of neurosurgery is effectively improved, and the technical effect of comprehensive and accurate index setting is achieved. Therefore, the technical problems that the preoperative evaluation of the neurosurgery depends on the experience and the inspection data of a doctor, the influence of the level of the doctor is large, the objective and comprehensive evaluation standard is lacked, the evaluation result is not comprehensive enough, and the reliability is unstable in the prior art are solved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A method of preoperative assessment of neurosurgery, wherein said method comprises:
obtaining first user information;
obtaining a first operation scheme of a first user according to the first user information;
acquiring first user history information according to the first user information;
obtaining first surgical assessment information based on the first user's first surgical plan;
inputting the first operation scheme of the first user and the historical information of the first user into an index matching model to obtain first matching index information;
obtaining a first user evaluation rule according to the first operation evaluation information and the first matching index information;
obtaining first evaluation rule information and second evaluation rule information according to the first user evaluation rule, wherein the first evaluation rule information has a first attribute;
obtaining a first user detection requirement according to the first evaluation rule information;
generating a first user evaluation questionnaire according to the second evaluation rule information;
obtaining a first evaluation result according to the first user detection requirement and a first user evaluation rule;
obtaining a second evaluation result according to the first user evaluation questionnaire and the first user evaluation rule;
and obtaining the pre-operation assessment report of the first user according to the first assessment result and the second assessment result.
2. The method of claim 1, wherein said obtaining first surgical assessment information according to said first user first surgical plan comprises:
obtaining first surgical procedure information according to a first surgical plan of the first user;
obtaining a step difficulty coefficient according to the first operation step information;
judging whether the step difficulty coefficient meets a first preset condition or not;
when the first operation step information is satisfied, acquiring step history record information according to the first operation step information;
acquiring step influence parameters according to the step history record information;
obtaining a parameter influence coefficient according to the step influence parameter;
and obtaining the first operation evaluation information according to the step influence parameters and the parameter influence coefficients.
3. The method of claim 1, wherein said obtaining a first user evaluation rule based on said first surgical evaluation information, said first match indicator information, comprises:
obtaining a matching index name and matching index data according to the first matching index information;
obtaining a surgical index requirement according to the first surgical assessment information and the matching index name;
acquiring an index deviation value according to the matching index data and the operation index requirement;
acquiring an index influence degree according to the index deviation value and the first operation evaluation information;
judging whether the index influence degree meets a second preset condition or not;
and when the first user evaluation rule is satisfied, obtaining the first user evaluation rule according to the index deviation value, the matched index name and the index influence degree.
4. The method of claim 1, wherein the obtaining first evaluation rule information, second evaluation rule information, according to the first user evaluation rule, wherein the first evaluation rule information has a first attribute comprises:
obtaining a first user index name list according to the first user evaluation rule, wherein the first user index name list comprises N first user index names, and N is a natural number greater than 1;
according to all the first user index names, obtaining historical index source information respectively;
obtaining the first evaluation rule information according to the first attribute and the historical source information of the index;
and obtaining the second evaluation rule information according to the first user evaluation rule and the first evaluation rule information.
5. The method of claim 1, wherein the method comprises:
obtaining first user historical operation information according to the first user historical information;
obtaining operation relevance according to the historical operation information of the first user and the first operation scheme of the first user;
obtaining a correlation screening standard according to the operation correlation;
obtaining a first user historical operation screening result according to the relevance screening standard and the operation relevance of the first user historical operation information;
obtaining operation influence data according to the first user historical operation screening result;
obtaining second surgery evaluation information according to the surgery influence data and the first surgery evaluation information;
and obtaining the first user evaluation rule according to the second operation evaluation information and the first matching index information.
6. The method of claim 5, wherein said obtaining first user historical procedure information based on said first user historical information comprises:
when the historical operation information of the first user does not exist, obtaining first associated user information according to the first user information;
obtaining first associated historical surgical information according to the first associated user information;
when the first associated historical operation information exists, obtaining first associated historical information according to the first associated user information;
obtaining a first user association degree according to the first user history information and the first association history information;
judging whether the first user association degree meets a third preset condition or not;
when the first relevant historical operation screening result is met, relevant operation influence data are obtained according to the first relevant historical operation screening result;
and obtaining the second operation evaluation information according to the related operation influence data and the first operation evaluation information.
7. The method of claim 1, wherein said entering the first user first surgical plan, the first user historical information into an index matching model, obtaining first matching index information, comprises:
taking the first user first surgical plan as first input information;
taking the first user history information as second input information;
inputting the first input information and the second input information into an index matching model, wherein the index matching model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: the first input information, the second input information, and identification information identifying the first matching index information;
obtaining output information of the index matching model, wherein the output information comprises the first matching index information.
8. A preoperative evaluation system for neurosurgery, for use in the method of any of claims 1-7, wherein said system comprises:
a first obtaining unit configured to obtain first user information;
a second obtaining unit, configured to obtain a first surgical plan of a first user according to the first user information;
a third obtaining unit, configured to obtain first user history information according to the first user information;
a fourth obtaining unit configured to obtain first surgical assessment information according to the first surgical plan of the first user;
a first execution unit, configured to input the first surgical plan of the first user and the first user history information into an index matching model, and obtain first matching index information;
a fifth obtaining unit, configured to obtain a first user evaluation rule according to the first surgical evaluation information and the first matching index information;
a sixth obtaining unit, configured to obtain first evaluation rule information and second evaluation rule information according to the first user evaluation rule, where the first evaluation rule information has a first attribute and the second evaluation rule information has a second attribute;
a seventh obtaining unit, configured to obtain a first user detection requirement according to the first evaluation rule information;
an eighth obtaining unit configured to generate a first user evaluation questionnaire according to the second evaluation rule information;
a ninth obtaining unit, configured to obtain a first evaluation result according to the first user detection requirement and a first user evaluation rule;
a tenth obtaining unit, configured to obtain a second evaluation result according to the first user evaluation questionnaire and the first user evaluation rule;
an eleventh obtaining unit, configured to obtain the pre-operation assessment report of the first user according to the first assessment result and the second assessment result.
9. A preoperative evaluation system for neurosurgery comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of claims 1-7 are implemented when the program is executed by the processor.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113380392A (en) * | 2021-06-25 | 2021-09-10 | 南通市第一人民医院 | Visit management method and system based on gynecological examination safety assessment |
| CN113380391A (en) * | 2021-06-24 | 2021-09-10 | 南通市第一人民医院 | Intelligent management method and system for orthopedic implant |
| CN113404742A (en) * | 2021-07-09 | 2021-09-17 | 中国人民解放军火箭军工程大学 | Electro-hydraulic servo mechanism health assessment method and system based on test data |
| CN113889224A (en) * | 2021-12-07 | 2022-01-04 | 苏州康多机器人有限公司 | Training of operation prediction model and operation indication method |
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2021
- 2021-03-08 CN CN202110250668.1A patent/CN112967819A/en not_active Withdrawn
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113380391A (en) * | 2021-06-24 | 2021-09-10 | 南通市第一人民医院 | Intelligent management method and system for orthopedic implant |
| CN113380392A (en) * | 2021-06-25 | 2021-09-10 | 南通市第一人民医院 | Visit management method and system based on gynecological examination safety assessment |
| CN113404742A (en) * | 2021-07-09 | 2021-09-17 | 中国人民解放军火箭军工程大学 | Electro-hydraulic servo mechanism health assessment method and system based on test data |
| CN113404742B (en) * | 2021-07-09 | 2024-01-26 | 中国人民解放军火箭军工程大学 | Electro-hydraulic servo mechanism health assessment method and system based on test data |
| CN113889224A (en) * | 2021-12-07 | 2022-01-04 | 苏州康多机器人有限公司 | Training of operation prediction model and operation indication method |
| CN113889224B (en) * | 2021-12-07 | 2022-10-21 | 苏州康多机器人有限公司 | Training of operation prediction model and operation indication method |
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